RRepoGEO

REPOGEO REPORT · LITE

datawhalechina/diy-llm

Default branch main · commit 2e57a006 · scanned 6/16/2026, 8:37:00 PM

GitHub: 957 stars · 102 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface datawhalechina/diy-llm, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Add a LICENSE file to the repository root, choosing a standard open-source license (e.g., MIT, Apache-2.0, GPL-3.0) that aligns with Datawhale China's goals for this educational project.
  • highreadme#2
    Update README H1 to clearly state it's an LLM building course

    Why:

    CURRENT
    <h1>Diy-LLM</h1>
    COPY-PASTE FIX
    <h1>Diy-LLM: 系统性大语言模型构建课程 (Build LLMs from Scratch)</h1>
  • mediumtopics#3
    Add specific topics for 'LLM course' and 'from scratch' implementation

    Why:

    CURRENT
    gpu-programming, llm, nlp, rl, sft, transformer, triton
    COPY-PASTE FIX
    gpu-programming, llm, nlp, rl, sft, transformer, triton, llm-course, llm-from-scratch

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface datawhalechina/diy-llm
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
tensorflow/tensorflow
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. tensorflow/tensorflow · recommended 2×
  2. Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville · recommended 1×
  3. Neural Networks from Scratch in Python by Harrison Kinsley and Daniel Kukiela · recommended 1×
  4. The Annotated Transformer by Alexander Rush · recommended 1×
  5. Hugging Face Transformers Library · recommended 1×
  • CATEGORY QUERY
    What resources teach building large language models from scratch, covering full-stack implementation?
    you: not recommended
    AI recommended (in order):
    1. Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
    2. Neural Networks from Scratch in Python by Harrison Kinsley and Daniel Kukiela
    3. The Annotated Transformer by Alexander Rush
    4. Hugging Face Transformers Library
    5. Building a GPT from scratch by Andrej Karpathy
    6. OpenAI's GPT-2 and GPT-3 Papers
    7. DeepSpeed
    8. FSDP (Fully Sharded Data Parallel)

    AI recommended 8 alternatives but never named datawhalechina/diy-llm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking practical courses on LLM pre-training, distributed training, and GPU inference optimization.
    you: not recommended
    AI recommended (in order):
    1. DeepLearning.AI
    2. NVIDIA Deep Learning Institute (DLI)
    3. Hugging Face Transformers Library (huggingface/transformers)
    4. ONNX Runtime (microsoft/onnxruntime)
    5. bitsandbytes (TimDettmers/bitsandbytes)
    6. PyTorch (pytorch/pytorch)
    7. TensorFlow (tensorflow/tensorflow)
    8. TensorFlow Lite (tensorflow/tensorflow)
    9. TensorFlow Serving (tensorflow/serving)
    10. Fast.ai (fastai/fastai)

    AI recommended 10 alternatives but never named datawhalechina/diy-llm. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of datawhalechina/diy-llm?
    pass
    AI named datawhalechina/diy-llm explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts datawhalechina/diy-llm in production, what risks or prerequisites should they evaluate first?
    pass
    AI named datawhalechina/diy-llm explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo datawhalechina/diy-llm solve, and who is the primary audience?
    pass
    AI named datawhalechina/diy-llm explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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  • Brand-free category queries5 vs 2 in Lite
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